2  Preparing Raster Data

3 Load data on study area

3.1 Soil samples

# Load soil data from sampling locations
bern_data <- readr::read_csv(
  here::here("data-raw/soildata/berne_soil_sampling_locations.csv")
  )

# Display data
head(bern_data) |> knitr::kable()
site_id_unique timeset x y dataset dclass waterlog.30 waterlog.50 waterlog.100 ph.0.10 ph.10.30 ph.30.50 ph.50.100
4_26-In-005 d1968_1974_ptf 2571994 1203001 validation poor 0 0 1 6.071733 6.227780 7.109235 7.214589
4_26-In-006 d1974_1978 2572149 1202965 calibration poor 0 1 1 6.900000 6.947128 7.203502 7.700000
4_26-In-012 d1974_1978 2572937 1203693 calibration moderate 0 1 1 6.200000 6.147128 5.603502 5.904355
4_26-In-014 d1974_1978 2573374 1203710 validation well 0 0 0 6.600000 6.754607 7.200000 7.151129
4_26-In-015 d1968_1974_ptf 2573553 1203935 validation moderate 0 0 1 6.272715 6.272715 6.718392 7.269008
4_26-In-016 d1968_1974_ptf 2573310 1204328 calibration poor 0 0 1 6.272715 6.160700 5.559031 5.161655

The dataset on soil samples from Bern holds 13 variables for 1052 entries (more information here):

  • site_id_unique: The location’s unique site id.

  • timeset: The sampling year and information on sampling type for soil pH (no label: CaCl\(_2\) laboratory measurement, field: indicator solution used in field, ptf: H\(_2\)O laboratory measurement transferred by pedotransfer function).

  • x: The x (easting) coordinates in meters following the (CH1903/LV03) system.

  • y: The y (northing) coordinates in meters following the (CH1903/LV03) system.

  • dataset: Specification whether a sample is used for model calibration or validation (this is based on randomization to ensure even spatial coverage).

  • dclass: Soil drainage class

  • waterlog.30, waterlog.50, waterlog.100: Specification whether soil was water logged at 30, 50, or 100 cm depth (0 = No, 1 = Yes).

  • ph.0.10, ph.10.30, ph.30.50, ph.50.100: Average soil pH between 0-10, 10-30, 30-50, and 50-100 cm depth.

3.1.1 Covariate data

Now, let’s load all the covariates that we want to produce our soil maps.

# Get a list with the path to all raster files
list_raster <- 
  base::list.files(
    here::here("data-raw/geodata/covariates/"),
    full.names = T
  )

# Display data (lapply to clean names)
lapply(
  list_raster, 
  function(x) sub(".*/(.*)", "\\1", x)
  ) |> 
  unlist() |> 
  head(5) |> 
  print()
[1] "be_gwn25_hdist.tif" "be_gwn25_vdist.tif" "cindx10_25.tif"    
[4] "cindx50_25.tif"     "geo500h1id.tif"

The output above shows the first five raster files with rather cryptic names. For this tutorial, we do not need to know all of the 91 rasters files that we are loading here but in a scientific context, you should know your data better of course. Let’s look at one of these raster files to get a better feeling for our data:

# Load a raster file as example: Picking the slope profile at 2m resolution
raster_example <- terra::rast(list_raster[74])
raster_example
class       : SpatRaster 
dimensions  : 986, 2428, 1  (nrow, ncol, nlyr)
resolution  : 20, 20  (x, y)
extent      : 2568140, 2616700, 1200740, 1220460  (xmin, xmax, ymin, ymax)
coord. ref. : CH1903+ / LV95 
source      : Se_slope2m.tif 
name        : Se_slope2m 
min value   :    0.00000 
max value   :   85.11286

As shown in the output, a raster object has the following properties (among others, see ?terra::rast):

  • class: The class of the file, here a SpatRaster.

  • dimensions: The number of rows, columns, years (if temporal encoding).

  • resolution: The resolution of the coordinate system, here it is 20 in both axes.

  • extent: The extent of the coordinate system defined by min and max values on the x and y axes.

  • coord. ref.: Reference coordinate system. Here, the raster is encoded using the LV95 geodetic reference system from which the projected coordinate system CH1903+ is derived.

  • source: The name of the source file.

  • names: The name of the raster file (mostly the file name without file-specific ending)

  • min value: The lowest value of all cells.

  • max value: The highest value of all cells.

4 Mapping the study area

Now, let’s look at a visualisation of this raster file. Since we selected the slope at 2m resolution, we expect a relief-like map with a color gradient that indicates the steepness of the terrain.

# Plot raster example
terra::plot(raster_example)

# To have some more flexibility, we can plot this in the ggplot-style as such:
ggplot2::ggplot() +
  tidyterra::geom_spatraster(data = raster_example) +
  ggplot2::scale_fill_viridis_c(
    na.value = NA,
    option = "magma",
    name = "Slope (%) \n"
    ) +
  ggplot2::theme_bw() +
  ggplot2::scale_x_continuous(expand = c(0, 0)) +  # avoid gap between plotting area and axis
  ggplot2::scale_y_continuous(expand = c(0, 0)) +
  ggplot2::labs(title = "Slope of the Study Area")

Note

Note that the second plot has different coordinates than the upper one. That is because the data was automatically projected to the World Geodetic System (WGS84, ESPG: 4326).

This looks already interesting but we can put our data into a bit more context. For example, a larger map background would be useful to get a better orientation of our location. Also, it would be nice to see where our sampling locations are and to differentiate these locations by whether they are part of the calibration or validation dataset. Bringing this all together requires some more understanding of plotting maps in R. So, don’t worry if you do not understand everything in the code chunk below and enjoy the visualisations:

# To get our map working correctly, we have to ensure that all the input data
# is in the same coordinate system. Since our Berne data is in the Swiss 
# coordinate system, we have to transform the sampling locations to the 
# World Geodetic System first.

# For the raster:
r <- terra::project(raster_example, "+init=EPSG:4326")

# For the sampling locations:
# Function from Stackoverflow:
# https://stackoverflow.com/questions/49536664/r-transforming-coordinates-inside-the-data-frame
change_coords <-function(data, from_CRS, to_CRS) {
  
  # Load required package
  require(sp)
  
  # Turn string into CRS
  from_CRS = CRS(from_CRS)
  to_CRS   = CRS(to_CRS)
  
  new <- 
    as.data.frame(
      spTransform(
        SpatialPointsDataFrame(
          coords = data.frame(
            lon = data$lon,
            lat = data$lat),
          
          data = data.frame(
            id = data$id,
            lon_old = data$lon,
            lat_old = data$lat),
          proj4string = from_CRS), 
        to_CRS
        )
      )
  
  new <- 
    new |> 
    dplyr::select(coords.x1, coords.x2, id) |> 
    dplyr::rename(lon = coords.x1,
                  lat = coords.x2)
  
  return(new)
}

# Transform dataframes
coord_cal <- 
  bern_data |> 
  dplyr::filter(dataset == "calibration") |> 
  dplyr::select(site_id_unique, x, y) |> 
  dplyr::rename(id = site_id_unique, lon = x, lat = y) |> 
  change_coords(
    from_CRS = "+init=epsg:2056", 
    to_CRS = "+init=epsg:4326"
    )

coord_val <- 
  bern_data |> 
  dplyr::filter(dataset == "validation") |> 
  dplyr::select(site_id_unique, x, y) |> 
  dplyr::rename(id = site_id_unique, lon = x, lat = y) |> 
  change_coords(
    from_CRS = "+init=epsg:2056", 
    to_CRS = "+init=epsg:4326"
    )
# Loading packages to improve code readbility to avoid :: notation
# Note: This code may only work when installing the development branch of {leaflet}:
# remotes::install_github('rstudio/leaflet')
library(leaflet)
library(terra)

# Let's get a nice color palette now for easy reference
pal <- colorNumeric(
  "magma",
  values(r),
  na.color = "transparent"
  )

# Next, we build a leaflet map
leaflet() |> 
  # As base maps, use two provided by ESRI
  addProviderTiles(providers$Esri.WorldImagery, group = "World Imagery") |>
  addProviderTiles(providers$Esri.WorldTopoMap, group = "World Topo") |>
  # Add our raster file
  addRasterImage(
    r,
    colors = pal,
    opacity = 0.6,
    group = "raster"
    ) |>
  # Add markers for sampling locations
  leaflet::addCircleMarkers(
    data = coord_cal,
    lng = ~lon,  # Column name for x coordinates
    lat = ~lat,  # Column name for y coordinates
    group = "training",
    color = "black"
  ) |>
    leaflet::addCircleMarkers(
    data = coord_val,
    lng = ~lon,  # Column name for x coordinates
    lat = ~lat,  # Column name for y coordinates
    group = "validation",
    color = "red"
  ) |>
  # Add some layout and legend
  addLayersControl(
    baseGroups = c("World Imagery","World Topo"),
    position = "topleft",
    options = layersControlOptions(collapsed = FALSE),
    overlayGroups = c("raster", "training", "validation")
    ) |>
  addLegend(
    pal = pal,
    values = values(r),
    title = "Slope (%)")
Note

This plotting example is based to the one shown in the AGDS 2 tutorial “Handful of Pixels” on phenology and more information on using spatial data in R can be found in the Chapter on Geospatial data in R.

That looks great! At first glance, it is a bit crowded but once you scroll in you can investigate our study area quite nicely. You can check whether the slope raster file makes sense by comparing it against the base maps. Can you see how cliffs along the Aare river, hills, and even gravel quarries show high slopes? We also see that our validation dataset is nicely distributed across the area covered by the training dataset.

Now that we have played with a few visualizations, let’s get back to preparing our data. The {terra} package comes with the very useful tool to stack multiple raster on top of each other, if they are of the same spatial format. To do so, we just have to feed in the vector of file names list_raster:

# Load all files as one batch
all_rasters <- terra::rast(list_raster)
all_rasters
class       : SpatRaster 
dimensions  : 986, 2428, 91  (nrow, ncol, nlyr)
resolution  : 20, 20  (x, y)
extent      : 2568140, 2616700, 1200740, 1220460  (xmin, xmax, ymin, ymax)
coord. ref. : CH1903+ / LV95 
sources     : be_gwn25_hdist.tif  
              be_gwn25_vdist.tif  
              cindx10_25.tif  
              ... and 88 more source(s)
names       :  be_gw~hdist, be_gw~vdist, cindx10_25, cindx50_25, geo500h1id, geo500h3id, ... 
min values  :    0.4998779,       0.000,       -100,  -99.99999,          1,          0, ... 
max values  : 3181.2531475,    1165.709,        100,   57.94390,         99,         56, ...

Now, we do not want to have the covariates’ data from all cells in the raster file. Rather, we want to reduce our stacked rasters to the x and y coordinates for which we have soil sampling data. We can do this using the terra::extract() function. Then, we want to merge the two dataframes of soil data and covariates data by their coordinates. Since the order of the covariate data is the same as the Bern data, we can simply bind their columns with cbind():

# Extract coordinates from sampling locations
sampling_xy <- bern_data |> dplyr::select(x, y)

# From all rasters, extract values for sampling coordinates
covar_data <- 
  terra::extract(all_rasters,  # The raster we want to extract from
                 sampling_xy,  # A matrix of x and y values to extract for
                 ID = FALSE    # To not add a default ID column to the output
                 )

merged_data <- cbind(bern_data, covar_data)
head(merged_data) |> knitr::kable()
site_id_unique timeset x y dataset dclass waterlog.30 waterlog.50 waterlog.100 ph.0.10 ph.10.30 ph.30.50 ph.50.100 be_gwn25_hdist be_gwn25_vdist cindx10_25 cindx50_25 geo500h1id geo500h3id lgm lsf mrrtf25 mrvbf25 mt_gh_y mt_rr_y mt_td_y mt_tt_y mt_ttvar NegO PosO protindx Se_alti2m_std_50c Se_conv2m Se_curv25m Se_curv2m_fmean_50c Se_curv2m_fmean_5c Se_curv2m_s60 Se_curv2m_std_50c Se_curv2m_std_5c Se_curv2m Se_curv50m Se_curv6m Se_curvplan25m Se_curvplan2m_fmean_50c Se_curvplan2m_fmean_5c Se_curvplan2m_s60 Se_curvplan2m_s7 Se_curvplan2m_std_50c Se_curvplan2m_std_5c Se_curvplan2m Se_curvplan50m Se_curvprof25m Se_curvprof2m_fmean_50c Se_curvprof2m_fmean_5c Se_curvprof2m_s60 Se_curvprof2m_s7 Se_curvprof2m_std_50c Se_curvprof2m_std_5c Se_curvprof2m Se_curvprof50m Se_diss2m_50c Se_diss2m_5c Se_e_aspect25m Se_e_aspect2m_5c Se_e_aspect2m Se_e_aspect50m Se_MRRTF2m Se_MRVBF2m Se_n_aspect2m_50c Se_n_aspect2m_5c Se_n_aspect2m Se_n_aspect50m Se_n_aspect6m Se_NO2m_r500 Se_PO2m_r500 Se_rough2m_10c Se_rough2m_5c Se_rough2m_rect3c Se_SAR2m Se_SCA2m Se_slope2m_fmean_50c Se_slope2m_fmean_5c Se_slope2m_s60 Se_slope2m_s7 Se_slope2m_std_50c Se_slope2m_std_5c Se_slope2m Se_slope50m Se_slope6m Se_toposcale2m_r3_r50_i10s Se_tpi_2m_50c Se_tpi_2m_5c Se_tri2m_altern_3c Se_tsc10_2m Se_TWI2m_s15 Se_TWI2m_s60 Se_TWI2m Se_vrm2m_r10c Se_vrm2m terrTextur tsc25_18 tsc25_40 vdcn25 vszone
4_26-In-005 d1968_1974_ptf 2571994 1203001 validation poor 0 0 1 6.071733 6.227780 7.109235 7.214589 234.39087 1.2986320 -10.62191 -6.9658718 6 0 7 0.0770846 0.0184651 4.977099 1316.922 9931.120 58 98 183 1.569110 1.534734 0.0159717 0.3480562 -40.5395088 -0.0014441 -0.0062570 0.0175912 0.0002296 2.9204133 1.1769447 -1.9364884 0.0031319 -0.5886537 -0.0042508 -0.0445323 -0.0481024 -0.0504083 -0.1655090 1.5687343 0.6229440 -1.0857303 0.0007920 -0.0028067 -0.0382753 -0.0656936 -0.0506380 -0.0732220 1.6507173 0.7082230 0.8507581 -0.0023399 0.3934371 0.1770810 -0.9702092 -0.7929600 -0.5661940 -0.9939429 5.930607 6.950892 -0.2840056 -0.6084610 -0.2402939 -0.0577110 -0.7661251 1.562085 1.548762 0.3228087 0.2241062 0.2003846 4.000910 16.248077 0.9428899 0.6683306 0.9333237 0.7310556 0.8815832 0.3113754 1.1250136 0.3783818 0.5250366 0 -0.0940372 -0.0583917 10.319408 0.4645128 0.0032796 0.0049392 0.0011592 0.000125 0.0002450 0.6248673 0.3332805 1.784737 65.62196 6
4_26-In-006 d1974_1978 2572149 1202965 calibration poor 0 1 1 6.900000 6.947128 7.203502 7.700000 127.41681 1.7064546 -10.87862 -11.8201790 6 0 7 0.0860347 0.0544361 4.975796 1317.000 9931.672 58 98 183 1.568917 1.533827 0.0204794 0.1484705 19.0945148 -0.0190294 0.0021045 0.0221433 0.0000390 3.8783867 4.3162045 2.1377332 -0.0171786 0.1278165 -0.0119618 -0.0501855 -0.3270764 -0.1004921 -0.5133076 2.0736780 2.2502327 -0.3522736 -0.0073879 0.0070676 -0.0522900 -0.3492197 -0.1005311 -0.4981292 2.1899190 2.4300070 -2.4900069 0.0097907 0.4014700 0.7360508 0.5683194 0.8753148 -0.3505180 0.3406741 5.984921 6.984581 -0.5732749 0.4801802 0.4917848 -0.4550385 0.7722272 1.543384 1.558683 0.2730940 0.2489859 0.2376962 4.001326 3.357315 1.0895698 0.9857153 1.0231543 1.0398037 1.0152543 0.5357812 1.3587183 0.0645478 0.5793087 0 -0.0014692 0.0180000 12.603136 0.5536283 0.0070509 0.0067992 0.0139006 0.000300 0.0005389 0.7573612 0.3395441 1.832904 69.16074 6
4_26-In-012 d1974_1978 2572937 1203693 calibration moderate 0 1 1 6.200000 6.147128 5.603502 5.904355 143.41533 0.9372618 22.10210 0.2093917 6 0 7 0.0737963 3.6830916 4.986864 1315.134 9935.438 58 98 183 1.569093 1.543057 0.0048880 0.1112066 -9.1396294 0.0039732 0.0009509 0.0431735 0.0034232 0.7022317 0.4170935 -0.4178924 -0.0026431 -0.0183221 0.0015183 -0.0079620 0.0053904 -0.0091239 -0.0110896 0.3974485 0.2292406 -0.2168447 -0.0013561 -0.0024548 -0.0089129 -0.0377831 -0.0125471 -0.0052359 0.4158890 0.2700820 0.2010477 0.0012870 0.6717541 0.4404107 -0.6987815 -0.3866692 -0.1960597 -0.7592779 5.953919 6.990917 -0.3006475 -0.9221049 -0.9633239 -0.3257418 -0.9502072 1.565405 1.563151 0.2305476 0.2182523 0.1434273 4.000320 11.330072 0.5758902 0.5300468 0.5107915 0.5744110 0.4975456 0.2001768 0.7160403 0.1311051 0.4620202 0 0.0340407 -0.0145804 7.100000 0.4850160 0.0021498 0.0017847 0.0011398 0.000000 0.0000124 0.7978453 0.4455501 1.981526 63.57096 6
4_26-In-014 d1974_1978 2573374 1203710 validation well 0 0 0 6.600000 6.754607 7.200000 7.151129 165.80418 0.7653937 -20.11569 -7.7729993 6 0 7 0.0859686 0.0075817 5.285522 1315.160 9939.923 58 98 183 1.569213 1.542792 0.0064054 0.3710849 -0.9318936 -0.0371234 0.0029348 -0.1056513 0.0127788 1.5150748 0.2413423 -0.0289909 0.0020990 -0.0706228 -0.0113604 -0.0301961 -0.0346193 -0.0273140 -0.0343277 0.8245047 0.1029889 -0.0272214 -0.0041158 0.0257630 -0.0331309 0.0710320 -0.0400928 0.0529446 0.8635767 0.1616543 0.0017695 -0.0062147 0.4988544 0.4217250 -0.8485889 -0.8657616 -0.8836724 -0.8993938 4.856076 6.964162 -0.5735765 -0.4998477 -0.4677161 -0.4121092 -0.4782534 1.562499 1.562670 0.3859352 0.2732429 0.1554769 4.000438 42.167496 0.8873205 0.8635756 0.9015982 0.8518201 0.5767300 0.2149791 0.8482135 0.3928713 0.8432562 0 0.0686932 -0.0085602 8.303085 0.3951114 0.0008454 0.0021042 0.0000000 0.000100 0.0000857 0.4829135 0.4483251 2.113142 64.60535 6
4_26-In-015 d1968_1974_ptf 2573553 1203935 validation moderate 0 0 1 6.272715 6.272715 6.718392 7.269008 61.39244 1.0676192 -55.12566 -14.0670462 6 0 7 0.0650000 0.0007469 5.894688 1315.056 9942.032 58 98 183 1.570359 1.541979 0.0042235 0.3907509 4.2692256 0.0378648 0.0022611 -0.1020419 0.0161510 3.6032522 1.8169731 0.6409346 0.0346340 0.0476020 0.0378154 -0.0179657 -0.0137853 -0.0146946 0.0060875 1.4667766 0.9816071 0.2968794 0.0337645 -0.0000494 -0.0202268 0.0882566 -0.0308456 0.0929077 2.6904552 1.0218329 -0.3440553 -0.0008695 0.6999696 0.3944107 -0.8918364 -0.8864348 -0.7795515 -0.4249992 4.130917 6.945287 0.4304937 0.4614536 0.5919228 0.6559467 0.4574654 1.550528 1.562685 0.4330348 0.3299487 0.1889674 4.000948 5.479310 1.8937486 1.2098556 1.5986075 1.2745584 2.7759163 0.5375320 1.2301254 0.3582314 1.1426100 0 0.3005829 0.0061576 10.110727 0.5134069 0.0043268 0.0045225 0.0054557 0.000200 0.0002062 0.6290755 0.3974232 2.080674 61.16533 6
4_26-In-016 d1968_1974_ptf 2573310 1204328 calibration poor 0 0 1 6.272715 6.160700 5.559031 5.161655 310.05014 0.1321367 -17.16055 -28.0693741 6 0 7 0.0731646 0.0128017 5.938320 1315.000 9940.597 58 98 183 1.569434 1.541606 0.0040683 0.1931891 -0.1732794 -0.1602274 -0.0035833 -0.1282881 0.0003549 1.5897882 0.8171870 0.0318570 -0.0123340 0.0400775 -0.0813964 -0.0049875 0.0320331 -0.0049053 0.0374298 0.7912259 0.3455668 0.0100844 -0.0059622 0.0788309 -0.0014042 0.1603212 -0.0052602 0.0867119 1.0207798 0.6147888 -0.0217726 0.0063718 0.3157751 0.5292308 -0.8766075 0.5905659 0.8129975 0.1640853 2.030315 6.990967 0.6325440 0.8054439 0.5820994 0.7448481 0.6081498 1.563066 1.552568 0.3688371 0.2607146 0.1763995 4.000725 13.499996 1.0418727 0.8515157 1.2106605 0.8916541 1.2163279 0.4894866 1.0906221 0.2049688 0.7156029 0 -0.0910767 0.0034276 9.574804 0.3864355 0.0001476 0.0003817 0.0000000 0.000525 0.0001151 0.6997021 0.4278295 2.041467 55.78354 6

Great that worked without problems!

5 Spatial correlation

As touched in Chapter 1, we should find a way to take spatial auto-correlation into account. Currently, our dataset holds spatial information in the x- and y-coordinates. The risk of including these coordinates is that a model learns the spatial features of our study area but not the generalisable underlying processes that drive soil development. For example, if our study area has a tendency to have a given soil type towards the north, a model may just learn this and predict a higher probability of that soil type with increasing y-coordinates. This tendency, however, may not be always true (for more, see Chapter 6).

Another consequence of just using x- and y-coordinates is that, when we use that model for prediction, the final map will most-likely look like a chess-board. Although it does not alleviate the issue of spatial autocorrelation, a quick fix to avoid a chess-board pattern is to add rotated coordinates to your dataset. That means that for every pixel, you rotate its x- and y-coordinates by a certain angle (see Figure Figure 5.1).1 This allows a Random Forest model to not just split across the orthogonal x- and y-coordinates but also along the rotated axes. Here, we are going to shift the coordinate system by 30° and 60° as follows:

final_data <- 
  merged_data |> 
    dplyr::mutate(
      x30 = x*cos(30/180*pi) - y*sin(30/180*pi), # 30/180*pi = 30°
      y30 = x*sin(30/180*pi) + y*cos(30/180*pi),
      x60 = x*cos(60/180*pi) - y*sin(60/180*pi), # 30/180*pi = 60°
      y60 = x*sin(60/180*pi) + y*cos(60/180*pi)
    )

Figure 5.1: Visualisation of rotating x- and y-coordinates by 30° and 60°. Figure adapted from work by M. Nussbaum.

Note that not all our covariates may be continuous variables and therefore have to be encoded as factors. As an easy check, we can take the original corvariates data and check for the number of unique values in each raster. If the variable is continuous, we expect that there are a lot of different values - at maximum 1052 different values because we have that many entries. So, let’s have a look and assume that variables with 10 or less different values are categorical variables.

cat_vars <- 
  covar_data |> 
  # Get number of distinct values per variable
  dplyr::summarise(dplyr::across(dplyr::everything(), ~ dplyr::n_distinct(.))) |> 
  # Turn df into long format for easy filtering
  tidyr::pivot_longer(dplyr::everything(), 
                      names_to = "variable", 
                      values_to = "n") |> 
  # Filter out variables with 10 or less distinct values
  dplyr::filter(n <= 10) |>
  # Extract the names of these variables
  dplyr::pull('variable')

cat("Variables with less than 10 distinct values:", cat_vars)
Variables with less than 10 distinct values: geo500h1id geo500h3id lgm vszone

Now that we have the names of the categorical values, we can mutate these columns in our df using the base function as.factor():

final_data <- 
  final_data |> 
  dplyr::mutate(dplyr::across(cat_vars, ~ as.factor(.)))

6 Checking missing data

We are almost done with our data preparation, we just need to reduce it to sampling locations for which we have a decent amount of data on the covariates. Else, we blow up the model calibration with data that is not informative enough.

# Get number of rows to calculate percentages
n_rows <- nrow(final_data)

# Get number of distinct values per variable
final_data |> 
  dplyr::summarise(dplyr::across(dplyr::everything(), 
                                 ~ length(.) - sum(is.na(.)))) |> 
  tidyr::pivot_longer(dplyr::everything(), 
                      names_to = "variable", 
                      values_to = "n") |>
  dplyr::mutate(perc_available = round(n/n_rows *100)) |> 
  dplyr::arrange(perc_available) |> 
  head(10) |> 
  knitr::kable()
variable n perc_available
ph.30.50 856 81
ph.10.30 866 82
ph.50.100 859 82
timeset 871 83
ph.0.10 870 83
dclass 1006 96
site_id_unique 1052 100
x 1052 100
y 1052 100
dataset 1052 100

This looks good, we have no variable with a substantial amount of missing data. Generally, only pH measurements are lacking, which we should keep in mind when making predictions and inferences. Another great way to explore your data, is using the {visdat} package:

final_data |> visdat::vis_miss()

Alright, we see that we are not missing a lot of data for any variable and in total only 1% of our data is NA. That is good enough! We do not have to modify the dataframe any further and can save it for further analysis.

7 Save data

saveRDS(final_data, 
        here::here("data/bern_sampling_locations_with_covariates.rds"))

  1. For a detailed explanation, see Wikipedia article on axes rotation.↩︎